Our method comprises two main components: a crop row detection pipeline which predicts the central crop row which the robot will follow and a visual servoing controller which generates the velocity commands for the robot to follow. We propose a semantic segmentation approach for crop row detection based on U-Net [1]}. The labels were created in such a way that a crop row is represented as a single object rather than individual plants as shown in Figure REF . This representation will help the U-Net to predict the complete crop row despite the presence of discontinuities. The segmentation mask from U-Net is then used to identify the central crop row which the robot will follow. The visual servoing controller uses the angle and starting point of the detected crop row to drive the robot to the desired position. The overall architecture of our proposed visual servoing controller is illustrated in Figure REF . The architecture of the U-Net CNN used in our method is given in Figure REF .
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